The goal of this project is to develop a robust and transparent framework for assessing afforestation suitability in Iceland. Afforestation plays a key role in climate adaptation, land restoration, and biodiversity enhancement, but its success depends on finding locations where environmental conditions, social priorities, and policy goals align. This project aims to identify such areas through an integrated, data-driven modern approach that combines ecological modelling, remote sensing, and spatial decision analysis.
The workflow begins with a broad-scale ecological assessment that identifies general patterns of forest suitability across Iceland. This first stage captures large-scale influences such as climate and soil, providing a foundation for understanding where afforestation is biophysically feasible. A second, finer-scale model then refines these predictions using high-resolution satellite imagery and terrain data, capturing local variations in topography, microclimate, and land surface characteristics. Together, these models provide a multi-scale understanding of where trees are most likely to establish and thrive.
Building on these ecological layers, the analysis incorporates social and infrastructural factors such as accessibility and proximity to population centres. These elements ensure that ecologically suitable areas are also practical for implementation, management, and community engagement. The inclusion of such human dimensions reflects the reality that successful afforestation must balance environmental potential with social and logistical feasibility.
Finally, the project applies a probabilistic Multi-Criteria Decision Analysis (MCDA)(suitability ananlysis tool in ESRI) to integrate all dimensions of suitability—ecological, social, and infrastructural—into a unified framework. Unlike traditional MCDA approaches that rely on fixed weights, this probabilistic method samples from plausible weight ranges to represent uncertainty in the weighting process. This produces a spectrum of possible outcomes rather than a single deterministic map, supporting more flexible, risk-aware planning.
Overall, this framework provides a transparent and scientifically grounded foundation for afforestation planning in Iceland. By linking large-scale ecological modelling with fine-scale environmental data and social considerations, it enables policymakers and land managers to identify where afforestation can deliver the greatest ecological and societal benefits.
The ecological suitability assessment forms the foundation of the afforestation suitability analysis workflow. We employ a two-step modelling approach: an initial broad-scale assessment provides a general overview of suitable regions across Iceland, followed by a refinement stage that incorporates high-resolution environmental data (satellite and terrain) to capture local variability. This hierarchical framework ensures that both large-scale patterns and fine-scale environmental nuances are considered, resulting in more accurate and ecologically informed afforestation planning.
Ecological niche modelling
To identify ecologically suitable areas for afforestation in Iceland, we first employ a broad-scale species ecological niche model (ENM). ENMs are a tool used to predict where conditions are environmentally suitable to support species survival and reproduction. By relating known occurrences of a species (such as observation or survey data) to environmental variables like temperature, precipitation, soil type, or elevation, ENMs identify the combination of conditions that define the species’ ecological niche. These relationships can then be projected across space to estimate potential distributions—such as identifying suitable habitats in unsurveyed areas. ENMs are widely used in ecology, conservation planning, and biodiversity management to understand species–environment relationships and guide decision-making (Shilky et al. 2023).
Species occurrence data
Here, we used the Natturulegt birkilendi and the raektad skoglendi datasets to gain an understanding about the current distributions of naturally occuring Birch and cultivated forests in Iceland. Since this data is given as polygons, 5000 points were randomly sampled from the polygons for subsequent ENM. In practice, rather than combining all species together as done here, species-specific ENMs could be used to obtain individual species preferential habitats across Iceland. Future work could focus on this.
Environmental Predictor Variables
The ENM incorporated climate, soil, and topographic variables derived from multiple global datasets. Climate variables were obtained from WorldClim, soil properties from SoilGrids, and terrain attributes from the ArcticDEM and are shown below.
| Dataset | Description | Units |
|---|---|---|
| WorldClim | All 19 bioclimatic variables (temperature and precipitation predictors) | — |
| SoilGrids | Bulk density of the fine earth fraction | cg/cm³ |
| SoilGrids | Cation Exchange Capacity of the soil | mmol(c)/kg |
| SoilGrids | Volumetric fraction of coarse fragments (>2 mm) | cm³/dm³ (vol‰) |
| SoilGrids | Proportion of clay particles (<0.002 mm) | g/kg |
| SoilGrids | Total nitrogen (N) | cg/kg |
| SoilGrids | Soil pH | pHx10 |
| SoilGrids | Proportion of sand particles (>0.05 mm) | g/kg |
| SoilGrids | Proportion of silt particles (0.002–0.05 mm) | g/kg |
| SoilGrids | Soil organic carbon content in the fine earth fraction | dg/kg |
| SoilGrids | Organic carbon density | hg/dm³ |
| SoilGrids | Organic carbon stocks | t/ha |
| ArcticDEM | Terrain slope | degrees |
| ArcticDEM | Topographic Position Index | — |
Broad-scale Ecological suitability Predictions
ENM predictions were estimated across Iceland at a 1 km spatial resolution. These predictions highlight areas with the highest ecological suitability for afforestation, providing a foundation for more detailed, fine-scale planning. The predictions can be seen below, where 0 indicates low suitability for tree establishment and 1 indicates highly preferential.